COVID-19: An evidence-based and disaster response methodology

Abstract

The world is gradually getting out of the grip of COVID-19 pandemic, although there are still high number of cases in some countries. Most of the initial attempts to predict and project the course of pandemic were hypothetical or based on historical data, as no current and specific data were available in the early days of pandemic. Most governments followed the policy of “flattening the curve” in order to avoid overwhelming their health systems. Most of the world also followed the policy of forced lockdowns to stop the spread of the virus. However, these policies produced did not produce consistent results across the globe. To investigate the impact of various policy measures on the reported outcomes, this research analyzed the actual COVID-19 data up till May 29, 2021, and the associated outcomes. Using global COVID-19 death rate as a base, the death rates of various countries were analyzed to gauge the efficacy of lockdown measures through probabilistic estimates and relative lack of uncertainty. Brier score was calculated to find the accuracy of probabilistic estimates. The data show high divergence in infection, death, and growth rates of the virus in different countries. The research also includes comparing the effects of virus in year 2020 and 2021, and the effect of vaccination. It can be seen that the collective world response was not commensurate with the actual risks involved. The paper concludes by emphasizing the need for specific evidence-based governance and disaster response management to face similar challenges in the future.